




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Optional[Any]          =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """mvp"""
						UpperCamelCase      =       ["""past_key_values"""]
						UpperCamelCase      =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

						def __init__(		self :List[str]     ,			__snake_case :List[Any]=5_02_67     ,			__snake_case :List[str]=10_24     ,			__snake_case :Optional[int]=12     ,			__snake_case :Union[str, Any]=40_96     ,			__snake_case :List[Any]=16     ,			__snake_case :Union[str, Any]=12     ,			__snake_case :int=40_96     ,			__snake_case :Optional[int]=16     ,			__snake_case :List[Any]=0.0     ,			__snake_case :Union[str, Any]=0.0     ,			__snake_case :Any="gelu"     ,			__snake_case :int=10_24     ,			__snake_case :Tuple=0.1     ,			__snake_case :Optional[Any]=0.0     ,			__snake_case :Tuple=0.0     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Dict=0.0     ,			__snake_case :Union[str, Any]=False     ,			__snake_case :List[str]=True     ,			__snake_case :List[str]=1     ,			__snake_case :Optional[int]=0     ,			__snake_case :Optional[Any]=2     ,			__snake_case :int=True     ,			__snake_case :List[str]=2     ,			__snake_case :Any=2     ,			__snake_case :Union[str, Any]=False     ,			__snake_case :int=1_00     ,			__snake_case :Optional[Any]=8_00     ,			**__snake_case :Optional[int]     ,			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=vocab_size
										__magic_name__						:						str										=max_position_embeddings
										__magic_name__						:						Dict										=d_model
										__magic_name__						:						Any										=encoder_ffn_dim
										__magic_name__						:						str										=encoder_layers
										__magic_name__						:						Tuple										=encoder_attention_heads
										__magic_name__						:						int										=decoder_ffn_dim
										__magic_name__						:						Dict										=decoder_layers
										__magic_name__						:						Tuple										=decoder_attention_heads
										__magic_name__						:						Dict										=dropout
										__magic_name__						:						str										=attention_dropout
										__magic_name__						:						Tuple										=activation_dropout
										__magic_name__						:						List[Any]										=activation_function
										__magic_name__						:						List[Any]										=init_std
										__magic_name__						:						List[Any]										=encoder_layerdrop
										__magic_name__						:						Optional[Any]										=decoder_layerdrop
										__magic_name__						:						List[Any]										=classifier_dropout
										__magic_name__						:						Dict										=use_cache
										__magic_name__						:						Optional[int]										=encoder_layers
										__magic_name__						:						Optional[Any]										=scale_embedding  # scale factor will be sqrt(d_model) if True
										__magic_name__						:						Any										=use_prompt
										__magic_name__						:						Tuple										=prompt_length
										__magic_name__						:						List[Any]										=prompt_mid_dim

										super().__init__(
										    pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			is_encoder_decoder=__snake_case     ,			decoder_start_token_id=__snake_case     ,			forced_eos_token_id=__snake_case     ,			**__snake_case     ,			)

										if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""     ,			__snake_case			):
														__magic_name__						:						Optional[int]										=self.bos_token_id
														warnings.warn(
														    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
														    """The config can simply be saved and uploaded again to be fixed."""			)







from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	'''pixel_values'''
							UpperCAmelCase_						:		List[str]								=	False
							UpperCAmelCase_						:		Tuple								=	TimmBackboneConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														requires_backends(self    ,						'''timm'''						)
														super().__init__(__UpperCAmelCase						)
														A      :					List[str]      		=					config

														if config.backbone is None:
																					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''						)

														if config.backbone not in timm.list_models():
																					raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

														if hasattr(__UpperCAmelCase    ,						'''out_features'''						) and config.out_features is not None:
																					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''						)

														A      :					str      		=					getattr(__UpperCAmelCase    ,						'''use_pretrained_backbone'''    ,						__UpperCAmelCase						)
														if pretrained is None:
																					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''						)

														# We just take the final layer by default. This matches the default for the transformers models.
														A      :					Optional[int]      		=					config.out_indices if getattr(__UpperCAmelCase    ,						'''out_indices'''    ,						__UpperCAmelCase						) is not None else (-1,)

														A      :					str      		=					timm.create_model(
														    config.backbone    ,						pretrained=__UpperCAmelCase    ,						features_only=config.features_only    ,						in_chans=config.num_channels    ,						out_indices=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														A      :					str      		=					self._backbone.return_layers
														A      :					Any      		=					{layer['''module''']: str(__UpperCAmelCase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
														super()._init_backbone(__UpperCAmelCase						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														requires_backends(cls    ,						['''vision''', '''timm''']						)
														from ...models.timm_backbone import TimmBackboneConfig

														A      :					Optional[Any]      		=					kwargs.pop('''config'''    ,						TimmBackboneConfig()						)

														A      :					str      		=					kwargs.pop('''use_timm_backbone'''    ,						__UpperCAmelCase						)
														if not use_timm:
																					raise ValueError('''use_timm_backbone must be True for timm backbones'''						)

														A      :					Optional[int]      		=					kwargs.pop('''num_channels'''    ,						config.num_channels						)
														A      :					List[str]      		=					kwargs.pop('''features_only'''    ,						config.features_only						)
														A      :					Optional[int]      		=					kwargs.pop('''use_pretrained_backbone'''    ,						config.use_pretrained_backbone						)
														A      :					Optional[int]      		=					kwargs.pop('''out_indices'''    ,						config.out_indices						)
														A      :					int      		=					TimmBackboneConfig(
														    backbone=__UpperCAmelCase    ,						num_channels=__UpperCAmelCase    ,						features_only=__UpperCAmelCase    ,						use_pretrained_backbone=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						)
														return super()._from_config(__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														pass






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       Union[BackboneOutput, Tuple[Tensor, ...]]:
														A      :					Optional[int]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					str      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					Any      		=					output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError('''Cannot output attentions for timm backbones at the moment'''						)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					A      :					Dict      		=					self._all_layers
																					A      :					int      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Any      		=					self._return_layers
																					A      :					Union[str, Any]      		=					tuple(hidden_states[i] for i in self.out_indices						)
														else:
																					A      :					Optional[int]      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					List[Any]      		=					None

														A      :					str      		=					tuple(__UpperCAmelCase						)
														A      :					int      		=					tuple(__UpperCAmelCase						) if hidden_states is not None else None

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												A      :					Tuple      		=					output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						attentions=__UpperCAmelCase						)






